Unveiling Patterns of Generative AI Integration That Enhance Learning: A Systematic Review of Empirical Studies

Abstract

The surge in Generative AI (GenAI) research highlights its potential for learning. A growing body of empirical studies has examined the application of GenAI across diverse contexts. However, there remains an underexplored research field concerning how GenAI can be integrated into learning processes and what recurring patterns are observed across studies reporting positive outcomes. Addressing this gap, this systematic review synthesized a corpus of 315 eligible empirical studies with three aims: (a) synthesizing reported outcome directions associated with GenAI-mediated learning across diverse stakeholders and disciplinary contexts; (b) identifying the instructional stages in which GenAI is integrated and the functions it performs; and (c) linking these recurring patterns to learning-outcome evidence reported in the included studies. Findings revealed that empirical research remains concentrated in higher education and language-related disciplines, with predominantly positive reported outcome directions for affective and behavioral outcomes. Pattern analysis identified a guided generative feedback configuration that integrates the functional triad of content generation, teaching support, and feedback across the instructional implementation stages. Nonetheless, more variable cognitive and metacognitive outcomes point to possible boundary conditions, suggesting that positive reported outcome directions may depend on whether learners remain active agents in the learning process. This review provides an evidence-informed mapping of GenAI integration patterns in the reviewed corpus to inform future theory-driven research and evidence-based instructional practice in educational psychology.